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Course Outline
Introduction
- SciPy vs NumPy
- Overview of SciPy features and components
Getting Started
- Installing SciPy
- Understanding basic functions
Implementing Scientific Computing
- Using SciPy constants
- Calculating integrals
- Solving linear equations
- Creating matrices with sparse and graphs
- Optimizing or minimizing functions
- Performing significance tests
- Working with different file formats (Matlab, IDL, Matrix Market, etc.)
Visualizing and Manipulating Data
- Implementing K-means clustering
- Using spatial data structures
- Processing multidimensional images
- Calculating Fourier transformations
- Using interpolation for fixed data points
Troubleshooting
Summary and Next Steps
Requirements
- Python programming experience
Audience
- Developers
7 Hours
Testimonials (5)
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
pozytywna atmosfera na szkoleniu
Piotr Wojciechowski - Centrum Informatyki Resortu Finansów
Course - Data Mining with Python
examples based on our data
Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
Very interactive with various examples, with a good progression in complexity between the start and the end of the training.